Publication Date
12-1-2023
Journal
Neurospine
DOI
10.14245/ns.2347022.511
PMID
38171281
PMCID
PMC10762393
PubMedCentral® Posted Date
12-31-2023
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
Computer vision, Spinal fractures, Risk assessment, Deep learning, Machine learning
Abstract
Osteoporotic vertebral fractures (OVFs) are a significant health concern linked to increased morbidity, mortality, and diminished quality of life. Traditional OVF risk assessment tools like bone mineral density (BMD) only capture a fraction of the risk profile. Artificial intelligence, specifically computer vision, has revolutionized other fields of medicine through analysis of videos, histopathology slides and radiological scans. In this review, we provide an overview of computer vision algorithms and current computer vision models used in predicting OVF risk. We highlight the clinical applications, future directions and limitations of computer vision in OVF risk prediction.